NIS06-3: A Game Theoretic Approach to Detect Network Intrusions: The Cooperative Intruders Scenario
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider the problem of detecting intrusions initiated by cooperative malicious nodes in infrastructure-based networks. We achieve this objective by sampling a subset of the transmitted packets, between each intruder and the victim, over selected links or router interfaces. Here, the total sampling rate on all links must not exceed the sampling budget constraint. We build a game theoretic framework to model distributed network intrusions through multiple malicious nodes and a common victim node. To the best of our knowledge, there has not been any study for the case where the attack is distributed over cooperative intruders using game theory. Non-cooperative game theory is used to formally express the problem, where the two players are: (1) the intruders and (2) the intrusion detection system. Our game theoretic framework will guide the intruders to know their attack strategy and the IDS to have an optimal sampling strategy in order to detect these intrusion packets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it